


'''simple docstring'''



import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowercase      :		List[str]			=      logging.get_logger('transformers.models.speecht5')






def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  int		, _SCREAMING_SNAKE_CASE :  Any		, _SCREAMING_SNAKE_CASE :  Tuple  ):
					hf_model.apply_weight_norm()

					__a     :			Optional[Any]          =    checkpoint['''input_conv.weight_g''']
					__a     :			int          =    checkpoint['''input_conv.weight_v''']
					__a     :			Any          =    checkpoint['''input_conv.bias''']

					for i in range(len(config.upsample_rates  )  ):
										__a     :			List[str]          =    checkpoint[F"""upsamples.{i}.1.weight_g"""]
										__a     :			Optional[Any]          =    checkpoint[F"""upsamples.{i}.1.weight_v"""]
										__a     :			Tuple          =    checkpoint[F"""upsamples.{i}.1.bias"""]

					for i in range(len(config.upsample_rates  ) * len(config.resblock_kernel_sizes  )  ):
										for j in range(len(config.resblock_dilation_sizes  )  ):
															__a     :			Optional[int]          =    checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
															__a     :			Optional[Any]          =    checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
															__a     :			Any          =    checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

															__a     :			List[Any]          =    checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
															__a     :			List[Any]          =    checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
															__a     :			Optional[Any]          =    checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

					__a     :			Tuple          =    checkpoint['''output_conv.1.weight_g''']
					__a     :			Any          =    checkpoint['''output_conv.1.weight_v''']
					__a     :			int          =    checkpoint['''output_conv.1.bias''']

					hf_model.remove_weight_norm()






@torch.no_grad()
def     lowerCamelCase				(_SCREAMING_SNAKE_CASE :  Tuple		, _SCREAMING_SNAKE_CASE :  List[str]		, _SCREAMING_SNAKE_CASE :  str		, _SCREAMING_SNAKE_CASE :  Any=None		, _SCREAMING_SNAKE_CASE :  int=None		, ):
					if config_path is not None:
										__a     :			Optional[int]          =    SpeechTaHifiGanConfig.from_pretrained(_SCREAMING_SNAKE_CASE  )
					else:
										__a     :			Any          =    SpeechTaHifiGanConfig()

					__a     :			Any          =    SpeechTaHifiGan(_SCREAMING_SNAKE_CASE  )

					__a     :			List[str]          =    torch.load(_SCREAMING_SNAKE_CASE  )
					load_weights(orig_checkpoint['model']['generator']		, _SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE  )

					__a     :			Dict          =    np.load(_SCREAMING_SNAKE_CASE  )
					__a     :			Optional[int]          =    stats[0].reshape(-1  )
					__a     :			Tuple          =    stats[1].reshape(-1  )
					__a     :			Any          =    torch.from_numpy(_SCREAMING_SNAKE_CASE  ).float()
					__a     :			List[Any]          =    torch.from_numpy(_SCREAMING_SNAKE_CASE  ).float()

					model.save_pretrained(_SCREAMING_SNAKE_CASE  )

					if repo_id:
										print('Pushing to the hub...'  )
										model.push_to_hub(_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
	__lowercase      :		Any			=      argparse.ArgumentParser()
	parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
	parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
	parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
	parser.add_argument(
	    '--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
	)
	parser.add_argument(
	    '--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
	)

	__lowercase      :		Any			=      parser.parse_args()
	convert_hifigan_checkpoint(
	    args.checkpoint_path,
	    args.stats_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.push_to_hub,
	)








'''simple docstring'''



from collections import defaultdict
def        UpperCAmelCase (    A	:					int					):
							SCREAMING_SNAKE_CASE       :   List[Any]         =      1
							SCREAMING_SNAKE_CASE       :   Dict         =      True
							for v in tree[start]:
														if v not in visited:
																					ret += dfs(A					)
							if ret % 2 == 0:
														cuts.append(A					)
							return ret
def        UpperCAmelCase (    ):
							dfs(1					)


if __name__ == "__main__":
							lowerCAmelCase_      ,				lowerCAmelCase_  :							Dict             =    10, 9
							lowerCAmelCase_  :							Dict             =    defaultdict(list)
							lowerCAmelCase_  :							dict[int, bool]             =    {}
							lowerCAmelCase_  :							list[int]             =    []
							lowerCAmelCase_  :							Optional[Any]             =    0
							lowerCAmelCase_  :							Dict             =    [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
							for u, v in edges:
														tree[u].append(v)
														tree[v].append(u)
							even_tree()
							print(len(cuts) - 1)




